Prologue
As a nowadays trend we see high interest from organizations to utilize data analytics and adopt AI to save time, become more efficient, and drive innovation. But we also suspect that it can be challenging. It is mostly because organizations first need to prepare data for acceptable state to fully take advantage in data analytics with AI.
The fundamental challenge is that AI requires good data. And if your data lives in different locations, it can be hard to get to a single source of truth. To make matters more confusing, there are many different analytics and AI solutions, all of which promise to deliver maximum insight from business data.
So where do you get started?
You need a single, unified data platform for all your data. And that’s exactly why we chose to highlight Microsoft Fabric
- Unleashing the Power of Data: Study Shows 2/3 of Company Data Goes Unused, Secoda, 2024.
- Gartner Press Release, Gartner Survey Finds 61% of Organizations Are Evolving Their D&A Operating Model Because of AI Technologies, April 29,
- State of Data Report 2024, Hakkoda, 2024.
What is Microsoft Fabric?
Fabric is a single unified product that takes care everything an enterprise need for analytics: From the datalake all the way to the business user in Office. It includes Data Integration, Data Engineering, Data Warehousing, Data Sciences, Real Time analytics and Business intelligence.
Fabric is delivered as a single SaaS product that provides auto-integration, auto-optimization, common architecture, central governance and Office like experiences across all the workloads. Fabric is designed from the ground up for the age of AI.
With Microsoft Fabric, you can build a strong data foundation – that is, a unified view of all your data regardless of where it lives
Key Components
- OneLake Stores the data for your whole organization
- Data Factory Connects to external databases and services
- Data Warehouse Stores data in a format optimized for analytics
- Data Science Empowers users to complete end-to-end data science workflows
- Real-time Intelligence Enables the extraction of insights, visualizations, and actions on data
- Data Engineering Provides tools that build the systems you want for data collection, storage, processing, and analysis
- Copilot Once enabled, Copilot and other generative AI features bring new ways to transform and analyze data in Microsoft Fabric and Power BI
- Power BI Provides rich data visualization and self-service business intelligence
What are the steps to improve your data analytics capabilities?
1. Start using Power BI
You can turn data into immediate impact with Power BI. Power BI is one of the workloads integrated with Fabric, and available within your familiar productivity and business applications.As a result, you can create and surface powerful visualizations that turn data into immediate impact quickly and easily. Fabric is also customizable, so you can quickly identify and create relevant workflows that capture underlying patterns.
2. Create single source of truth
Centralize all your data using OneLake. As you build more modern data capabilities, you need a common view of your data to drive holistic decision-making. That’s why the foundation of Fabric is OneLake – a single unified, logical data lake for your whole organization. You’ll get one set of data that acts as a single source of truth, without moving or duplicating your data. Plus, it’s automatically indexed for discovery, sharing, governance and compliance.
3. Start innovating with AI
Take advantage of a complete analytics platform. AI is only as good as the data it’s based on. As you enter the future, you need a data estate capable of fueling AI innovation across your organization.Fabric is the perfect platform to get your data ready for building custom AI experiences. From built-in Copilot solutions to custom machine learning models, Fabric delivers the foundation you need. And we can help you prepare, and model your data so you’re ready to take advantage of all AI has to offer.
Summary
In our experience, Fabric is a great option for businesses that need to be mindful of their budget and resources. Not only are AI capabilities built in when you’re ready, but it’s simple to use and cost-effective to deploy, while powering enterprise-grade analytics capabilities. It also requires a low up-front investment, but can scale to meet your growing data analytics usage.
